Users are increasingly consuming content electronically, such as by accessing digital content provided over the Internet or another such network. Users often rely upon search queries or keyword strings that can be used to identify potentially relevant content. In many instances, however, the relevance depends at least in part to the actual query that was submitted, as well as the way in which the potentially relevant content is categorized or identified. There often is no easy way for a user to modify a query to express a desired refinement, and no accurate way for that expression to be implemented so as to locate the content of interest. This is particularly true for visual attributes that may be subjective and that are difficult to quantify through existing keyword-based approaches.